Differentiating decompensation detection based on co-morbidities in heart failure

ABSTRACT

This document discusses, among other things, a system comprising a sensor signal processor configured to receive a plurality of electrical sensor signals produced by a plurality of sensors and at least one sensor signal produced by an implantable sensor, a memory that includes information indicating a co-morbidity of a subject, a sensor signal selection circuit that selects a sensor signal to monitor from among the plurality of sensor signals, according to an indicated co-morbidity, a threshold adjustment circuit that adjusts a detection threshold of the selected sensor signal according to the indicated co-morbidity, and a decision circuit that applies the adjusted detection threshold to the selected sensor signal to determine whether an event associated with worsening heart failure (HF) occurred in the subject and outputs an indication of whether the event associated with worsening HF occurred to a user or process.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/714,402, filed May 18, 2015, which is a continuation of U.S. patentapplication Ser. No. 14/140,710, filed Dec. 26, 2013, now issued as U.S.Pat. No. 9,037,222, which is a continuation of U.S. patent applicationSer. No. 13/848,869, filed Mar. 22, 2013, now issued as U.S. Pat. No.8,644,916, which is a continuation of U.S. patent application Ser. No.12/249,817, now U.S. Pat. No. 8,417,325, filed Oct. 10, 2008, whichclaims the benefit under 35 U.S.C. §119(e) of U.S. Provisional PatentApplication Ser. No. 60/979,749, filed on Oct. 12, 2007, each of whichis incorporated herein by reference in its entirety.

BACKGROUND

Implantable medical devices (IMDs) include devices designed to beimplanted into a patient. Some examples of these devices include cardiacfunction management (CFM) devices such as implantable pacemakers,implantable cardioverter defibrillators (ICDs), cardiacresynchronization therapy devices (CRTs), and devices that include acombination of such capabilities. The devices can be used to treatpatients using electrical or other therapy or to aid a physician orcaregiver in patient diagnosis through internal monitoring of apatient's condition. The devices may include one or more electrodes incommunication with one or more sense amplifiers to monitor electricalheart activity within a patient, and often include one or more sensorsto monitor one or more other internal patient parameters. Other examplesof implantable medical devices include implantable diagnostic devices,implantable drug delivery systems, or implantable devices with neuralstimulation capability.

Additionally, some IMDs detect events by monitoring electrical heartactivity signals. Some IMDs derive measurements of hemodynamicparameters related to chamber filling and contractions from electricalsignals provided by sensors. Sometimes patients who receive IMDs haveexperienced heart failure (HF) decompensation or other events associatedwith worsening HF. Some patients have experienced repeated HFdecompensations. Symptoms associated with worsening HF include pulmonaryand/or peripheral edema, dilated cardiomyopathy, or ventriculardilation. Early attention to signs and symptoms of HF decompensation isneeded for the health of the patient and allows early initiation oftreatment.

OVERVIEW

This document relates generally to systems, devices, and methods formonitoring hemodynamic parameters of a patient or subject. A systemexample includes a sensor signal processor configured to receive aplurality of electrical sensor signals produced by a plurality ofsensors and at least one sensor signal produced by an implantablesensor, a memory including information indicating a co-morbidity of asubject, a sensor signal selection circuit configured to select a sensorsignal to monitor from among the plurality of sensor signals, accordingto an indicated co-morbidity, a threshold adjustment circuit configuredto adjust a detection threshold of the selected sensor signal accordingto the indicated co-morbidity, and a decision circuit configured toapply the adjusted detection threshold to the selected sensor signal todetermine whether an event associated with worsening HF occurred in thesubject and to output an indication of whether the event associated withworsening HF occurred to a user or process.

A method example includes receiving a plurality of sensor signals, eachof which include physiologic information and at least one sensor signalprovided by an implantable sensor, selecting at least one sensor signalto monitor according to a co-morbidity of a subject indicated in storedco-morbidity information, manually and/or automatically adjusting adetection threshold of the selected sensor signal according to theindicated co-morbidity, applying the detection threshold to the selectedsensor signal to determine whether an event associated with worsening HFoccurred, and providing an indication of whether the event associatedwith worsening HF occurred to a user or process.

This overview is intended to provide an overview of subject matter ofthe present patent application. It is not intended to provide anexclusive or exhaustive explanation of the invention. The detaileddescription is included to provide further information about the presentpatent application.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1 is an illustration of portions of a system that uses an 1 MB.

FIGS. 2A-B show an example of an 1 MB that does not use intravascularleads to sense cardiac signals.

FIGS. 3A-3D show graphs of example measurements taken from subjectsexperiencing HF leading up to the subjects undergoing HF decompensation.

FIG. 4 is a block diagram of an example of a system to monitor one ormore hemodynamic parameters of a subject.

FIG. 5 is a flow diagram of an example of a method to monitor one ormore hemodynamic parameters of a subject.

FIG. 6 is another block diagram of another example of a system tomonitor one or more hemodynamic parameters of a subject.

FIG. 7 is another block diagram of yet another example of a system tomonitor one or more hemodynamic parameters of a subject.

DETAILED DESCRIPTION

An implantable medical device (IMD) may include one or more of thefeatures, structures, methods, or combinations thereof described herein.For example, a cardiac monitor or a cardiac stimulator may beimplemented to include one or more of the advantageous features and/orprocesses described below. It is intended that such a monitor,stimulator, or other implantable or partially implantable device neednot include all of the features described herein, but may be implementedto include selected features that provide for unique structures and/orfunctionality. Such a device may be implemented to provide a variety oftherapeutic or diagnostic functions.

FIG. 1 is an illustration of portions of a system 100 that uses an IMD105. Examples of IMD 105 include, without limitation, a pacemaker, acardioverter, a defibrillator, a cardiac resynchronization therapy (CRT)device, and other cardiac monitoring and therapy delivery devices,including cardiac devices that include or work in coordination with oneor more neuro-stimulating devices, drugs, drug delivery systems, orother therapies. As one example, the system 100 shown is used to treat acardiac arrhythmia. The IMD 105 typically includes an electronics unitcoupled by one or more cardiac leads 110, 115, 125, to a heart of apatient or subject. The electronics unit of the IMD 105 typicallyincludes components that are enclosed in a hermetically-sealed canisteror “can.” The system 100 also typically includes an IMD programmer orother external system 190 that communicates one or more wireless signals185 with the IMD 105, such as by using radio frequency (RF) or by one ormore other telemetry methods.

The example shown includes right atrial (RA) lead 110 having a proximalend 111 and a distal end 113. The proximal end 111 is coupled to aheader connector 107 of the IMD 105. The distal end 113 is configuredfor placement in the RA in or near the atrial septum. The RA lead 110may include a pair of bipolar electrodes, such as an RA tip electrode114A and an RA ring electrode 114B. The RA electrodes 114A and 114B areincorporated into the lead body at distal end 113 for placement in ornear the RA, and are each electrically coupled to IMD 105 through aconductor extending within the lead body. The RA lead is shown placed inthe atrial septum, but the RA lead may be placed in or near the atrialappendage, the atrial free wall, or elsewhere.

The example shown also includes a right ventricular (RV) lead 115 havinga proximal end 117 and a distal end 119. The proximal end 117 is coupledto a header connector 107. The distal end 119 is configured forplacement in the RV. The RV lead 115 may include one or more of aproximal defibrillation electrode 116, a distal defibrillation electrode118, an RV tip electrode 120A, and an RV ring electrode 120B. Thedefibrillation electrode 116 is generally incorporated into the leadbody such as in a location suitable for supraventricular placement inthe RA and/or the superior vena cava. The defibrillation electrode 118is incorporated into the lead body near the distal end 119 such as forplacement in the RV. The RV electrodes 120A and 120B may form a bipolarelectrode pair and are generally incorporated into the lead body atdistal end 119. The electrodes 116, 118, 120A, and 120B are eachelectrically coupled to IMD 105, such as through one or more conductorsextending within the lead body. The proximal defibrillation electrode116, distal defibrillation electrode 118, or an electrode formed on thecan of IMD 105 allow for delivery of cardioversion or defibrillationpulses to the heart.

The RV tip electrode 120A, RV ring electrode 120B, or an electrodeformed on the can of IMD 105 allow for sensing an RV electrogram signalrepresentative of RV depolarizations and delivering RV pacing pulses. Insome examples, the IMD includes a sense amplifier circuit to provideamplification and/or filtering of the sensed signal. RA tip electrode114A, RA ring electrode 114B, or an electrode formed on the can of IMD105 allow for sensing an RA electrogram signal representative of RAdepolarizations and allow for delivering RA pacing pulses. Sensing andpacing allows the IMD 105 to adjust timing of the heart chambercontractions. In some examples, the IMD 105 can adjust the timing ofventricular depolarizations with respect to the timing of atrialdepolarizations by sensing electrical signals in the RA and pacing theRV at the desired atrial-ventricular (AV) delay time.

A left ventricular (LV) lead 125 can include a coronary pacing orsensing lead that includes an elongate lead body having a proximal end121 and a distal end 123. The proximal end 121 is coupled to a headerconnector 107. A distal end 123 is configured for placement or insertionin the coronary vein. The LV lead 125 may include an LV ring or tipelectrode 128A and an LV ring electrode 128B. The distal portion of theLV lead 125 is configured for placement in the coronary sinus andcoronary vein such that the LV electrodes 128A and 128B are placed inthe coronary vein. The LV electrodes 128A and 128B may form a bipolarelectrode pair and are typically incorporated into the lead body atdistal end 123. Each can be electrically coupled to IMD 105 such asthrough one or more conductors extending within the lead body. LV tipelectrode 128A, LV ring electrode 128B, or an electrode formed on thecan of the IMD 105 allow for sensing an LV electrogram signalrepresentative of LV depolarizations and delivering LV pacing pulses.

The IMDs may be configured with a variety of electrode arrangements,including transvenous, epicardial electrodes (i.e., intrathoracicelectrodes), and/or subcutaneous, non-intrathoracic electrodes,including can, header, and indifferent electrodes, and subcutaneousarray or lead electrodes (i.e., non-intrathoracic electrodes). Some IMDsare able to sense signals representative of cardiac depolarizationsusing electrodes without leads.

FIGS. 2A-B show an example of an IMD 200 that does not use intravascularleads to sense cardiac signals. FIG. 2A shows that the IMD 200 includesa thicker end 213 to hold the power source and circuits. The IMD 200also includes electrodes 225 and 227 for remote sensing of cardiacsignals. Cardioversion and/or defibrillation are provided throughelectrodes 215 and 217. FIG. 2B shows an example of the position of theIMD 200 within a patient.

Monitoring of electrical signals related to cardiac activity may provideearly detection of a heart failure (HF) decompensation event or otherevent associated with HF. HF patients also may have differentco-morbidities. Examples of co-morbidities include diabetes (DM),hypertension (HTN), apnea, myocardial infarction (MI), and chronicobstructive pulmonary disorder (COPD). An HF patient without aco-morbidity may exhibit HF differently than a patient with aco-morbidity. Also, an HF patient with a co-morbidity (e.g., diabetes)may exhibit HF differently than a patient with multiple co-morbidities(e.g., diabetes and hypertension).

Certain measurements related to hemodynamic performance can be derivedfrom electrical signals provided by sensors. An example of such ameasurement is heart rate variability (HRV) derived from intrinsicsignals sensed using a cardiac signal sensing circuit. HRV can bepresented as a standard deviation of sequential mean R-R intervals(SDANN), a measure of minimum heart rate, a measure of maximum heartrate, and a heart rate mean.

Differences in HF decompensation for patients will be reflected indifferences in the measurements. Differences may also be evident in apatient activity log or a footprint histogram. Therefore, detectioncriteria for monitoring different measurements for evidence of worseningHF may be different for patients with different co-morbidities. Thesedifferences will also be reflected in the HF diagnostics recorded byIMDs.

FIGS. 3A-3D show graphs of example measurements taken from subjectsexperiencing HF leading up to the subjects undergoing HF decompensation.FIG. 3A shows graphs 305 of measurements of an activity log versus time(in weeks) for subjects taken during a period before a HF decompensationevent (at time=0). Graph 310 includes measurements of activity forsubjects that experienced HF, did not undergo decompensation during theperiod, and were non-diabetic. Graph 312 includes activity for subjectsthat experienced HF, did not undergo decompensation during the period,and were diabetic. Graph 315 includes activity for subjects thatexperienced HF, underwent decompensation, and were non-diabetic. Graph320 includes activity for subjects that experienced HF, underwentdecompensation, and were diabetic.

The graphs 305 show that there is a difference in activity during theperiod between subjects that did undergo HF decompensation and thosethat did not undergo decompensation during the period. Thus monitoringactivity such as by an activity log may help anticipate HFdecompensation in subjects with HF. The graphs 305 also show that, forsubjects that experienced HF decompensation, there is a difference inactivity level between subjects that have a co-morbidity of diabetes andsubjects that don't have diabetes. The graphs 305 further show thatthere is a difference in the slope of the activity levels betweensubjects having a co-morbidity of diabetes and subjects that don't havediabetes. Therefore, a small decrease in activity may have moresignificance as an indicator of an HF event for a patient with diabetesthan for a patient without diabetes.

FIG. 3B shows graphs 325 of measurements of minimum heart rate (HRMin)versus time taken during a period before a HF decompensation event.Graph 330 includes HRMin for subjects that experienced HF, did notundergo decompensation during the period, and were non-diabetic. Graph332 includes HRMin for subjects that experienced HF, did not undergodecompensation during the period, and were diabetic. Graph 335 includesHRMin for subjects that experienced HF, underwent decompensation, andare non-diabetic. Graph 340 includes HRMin for subjects that experiencedHF, underwent decompensation, and were diabetic.

The graphs 325 show that HRMin increased during the period for subjectsthat underwent decompensation. The increase may be due to thedecompensated heart trying to increase output. However, HRMin increasedless for those subjects that underwent decompensation and had diabetes.The graphs 345 also show that the slopes of the change in HRMin duringthe period are different between subjects having a co-morbidity ofdiabetes and subjects that don't have diabetes.

FIG. 3C shows graphs 345 of measurements of the standard deviation ofsequential mean R-R intervals (SDANN) versus time taken during a periodbefore a HF decompensation event. Graph 350 includes SDANN for subjectsthat experienced HF, did not undergo decompensation during the period,and were non-diabetic. Graph 352 includes SDANN for subjects thatexperienced HF, did not undergo decompensation during the period, andwere diabetic. Graph 355 includes SDANN for subjects that experiencedHF, underwent decompensation, and are non-diabetic. Graph 360 includesSDANN for subjects that experienced HF, underwent decompensation, andwere diabetic. The graphs 345 show that a five percent change in SDANNmay not be significant for a non-diabetic HF patient, but could be anindicator of a potential HF event in a HF patient with diabetes.

FIG. 3D shows graphs 365 of measurements of mean heart rate (HRMean)versus time taken during a period before a HF decompensation event.Graph 370 includes HRMean for subjects that experienced HF, did notundergo decompensation during the period, and were non-diabetic. Graph332 includes HRMean for subjects that experienced HF, did not undergodecompensation during the period, and were diabetic. Graph 375 includesHRMean for subjects that experienced HF, underwent decompensation, andare non-diabetic. Graph 380 includes HRMean for subjects thatexperienced HF, underwent decompensation, and were diabetic. The graphs365 show that HRMean increased during the period for subjects thatunderwent decompensation. Graph 380 shows that the HRMean increased lessfor those subjects that underwent decompensation and had diabetes. Thegraphs 365 also show that the slopes of the change in HRMean during theperiod are different between subjects having a co-morbidity of diabetesand subjects that don't have diabetes.

The example measurements in FIGS. 3A-3D show that measurements ofhemodynamic parameters leading up to an event associated with HF may bedifferent for individuals with co-morbidities (e.g., diabetes) than forindividuals without co-morbidities, and may be different for patientswith different combinations of co-morbidities. The examples also showthat device-based diagnostics may be able to reflect the differencescaused by co-morbidities if the devices are sensitive to thosedifferences.

FIG. 4 is a block diagram of an example of a system 400 to monitor oneor more hemodynamic parameters of a subject. The system 400 includes asensor signal processor 405, a memory 410, and a sensor signal selectioncircuit 415. The sensor signal processor 405 receives a plurality ofelectrical sensor signals produced by a plurality of sensors. Thesensors may be in electrical communication with the sensor signalprocessor 405 by being integral to the system 400, electricallyconnected to the system, or remote from the system 400 and provide asensor signal by a wireless communication method. At least one sensorsignal is produced by an implantable sensor, such as a sensor includedin an implantable medical device (IMD) for example.

The memory 410 includes information indicating a co-morbidity of thesubject or patient. The sensor signal selection circuit 415 is inelectrical communication with the sensor signal processor 405 and thememory 410. The sensor signal selection circuit 415 selects a sensorsignal to monitor from among the plurality of sensor signals, accordingto an indicated co-morbidity.

For example, if the information indicates that the subject is adiabetic, the sensor signal selection circuit 415 optionally selects aglucose sensor signal to monitor. If the information indicates that thesubject experiences apnea or hypopnea, the sensor signal selectioncircuit 415 optionally selects a respiration sensor signal indicative ofrespiration or breathing, such as from a respiration sensor or a pulseoximeter sensor. If the information indicates the patient haschronic-obstructive pulmonary disorder (COPD), sensor signal selectioncircuit 415 optionally selects an airflow sensor signal to monitor.

In some examples, selecting a sensor signal includes enabling the sensorproducing the signal and/or enabling a sensor interface circuit. In someexamples, if no co-morbidity is indicated, the sensor signal selectioncircuit 415 selects one or more default sensor signals to monitor anduses a default sensor detection threshold of the default sensor signalwhen no co-morbidity is indicated. A user is also able to select orremove sensor signals to monitor.

The sensor signal may be added to a default set of sensor signals when aco-morbidity is indicated. For example, monitoring the glucose sensorsignal may be added to monitoring a cardiac depolarization produced by acardiac signal sensor. In certain examples, the sensor signal selectioncircuit 415 selects a sensor signal to monitor according to an indicatedco-morbidity in addition to sensors selected or programmed formonitoring by a physician.

The system 400 also includes a threshold adjustment circuit 420 and adecision circuit 425. The threshold adjustment circuit 420 adjusts adetection threshold of the selected sensor signal according to theindicated co-morbidity.

For example, the sensor signal processor 405 may receive an activitysensor signal from an activity sensor, such as an accelerometer. Theactivity sensor signal is representative of activity of the subject. Thesensor signal processor 405 may use the activity sensor signal tomonitor sensed activity of the subject in an activity log. As in FIG.3A, if the activity log indicates that the activity of the subjectdecreases below a predetermined threshold level for a predeterminedperiod of time, or the rate or slope of activity is decreasing fasterthan a predetermined rate, this may indicate a worsening condition of HFfor the subject. If the co-morbidity information indicates aco-morbidity for the subject (e.g., diabetes), the threshold adjustmentcircuit 420 may vary the threshold level or rate such that the algorithmis more sensitive to small changes in activity.

Additionally, the memory 410 may include information indicating acardiovascular condition of the subject. The threshold adjustmentcircuit 420 adjusts a detection threshold of the selected sensor signalaccording to the indicated cardiovascular condition.

For example, the sensor signal processor 405 may receive a cardiacsignal representative of cardiac depolarizations of the subject. Thesensor signal processor 405 monitors the minimum heart rate or a meanheart rate of the subject. If the minimum or mean heart rate increasesabove a predetermined threshold heart rate, or is increasing with aslope faster than a predetermined threshold slope, this may be anindication of a worsening condition of HF for the subject. If theco-morbidity information indicates a co-morbidity for the subject, thethreshold adjustment circuit 420 may decrease the predeterminedthreshold heart rate or predetermined threshold heart rate slope todetect the indication of a worsening condition of HF. Optionally, thethreshold adjustment circuit 420 may use the cardiovascular conditioninformation (e.g., an indication that the subject is receiving cardiacresynchronization therapy (CRT)) to decrease the predetermined thresholdheart rate to detect the indication. The threshold adjustment circuit420 may also decrease the threshold using the co-morbidity informationin conjunction with the cardiovascular condition information.

In accordance with HF detection criteria, the decision circuit 425applies the adjusted detection threshold to the selected sensor signalto determine whether an event associated with worsening heart failure(HF) occurred in the subject. The decision circuit 425 outputs anindication of whether the event associated with worsening HF occurred toa user or process.

In some examples, the system 400 includes a plurality of sensors 430.Each sensor is configured to produce an electrical sensor signal thatincludes physiologic information of a subject. At least one of thesensors is an implantable sensor. The sensor signal selection circuit415 may select a sensor signal according to an indicated co-morbidity.For example, the plurality of sensors 430 may include an activity sensor(e.g., an accelerometer) that produces the signal representative ofactivity of the subject describes previously. The sensor signalselection circuit 415 selects the activity signal to monitor accordingto an indicated co-morbidity (e.g., diabetes).

In some examples, the plurality of sensors 430 includes a cardiac signalsensor that produces a sensor signal representative of an intrinsiccardiac signal of the subject. An example of a cardiac signal sensorincludes electrodes and sense amplifiers described in regard to FIG. 1.Another example of a cardiac signal sensing circuit is an ECG sensingcircuit. The ECG sensing circuit may be an external device and maycommunicate with the system using a wired interface (e.g., UniversalSerial Bus interface) or a wireless interface (e.g., radio frequency).Another example of a cardiac signal sensing circuit is a wireless ECGcircuit. A wireless ECG circuit produces a signal approximating thesurface ECG and is acquired without using surface (skin contact)electrodes. An example of a circuit for sensing the wireless ECG isdiscussed in commonly assigned, U.S. Patent Application Publication No.20050197674, entitled “Wireless ECG in Implantable Devices,” filed onMar. 5, 2004, which is incorporated herein by reference in its entirety.

As described previously, the sensor signal processor 405 may receive thesignal produced by the cardiac signal sensor to monitor heart rate. Inanother example, the sensor signal processor 405 may use the cardiacsignal sensor to monitor heart rate variability (HRV). HRV refers to thevariability of the time intervals between successive heart beats duringa sinus rhythm. In some examples of the system 400, the first conditionincludes a detected decrease in HRV. A patient with a low amount ofmeasured HRV implies that the patient may have a decreased ability tocompensate for changes in arterial pressure. Systems and methods tomeasure HRV are described in Carlson et al., U.S. Pat. No. 7,248,919,entitled “Cardiac Rhythm Management System Using Time Domain Heart RateVariability Indicia,” filed Dec. 2, 2003, which is incorporated hereinby reference in its entirety. If the co-morbidity information indicatesa co-morbidity for the subject, the threshold adjustment circuit 420 maydecrease a predetermined variability threshold to determine that anevent associated with worsening HF occurred.

In yet another example, the sensor signal processor 405 may monitor HRVby measuring the SDANN of the subject. If the SDANN decreases below apredetermined threshold SDANN or is decreasing at a predetermined rateor slope, this may be an indication of a worsening condition of HF forthe subject. If the co-morbidity information indicates a co-morbidityfor the subject, the threshold adjustment circuit 420 may decrease thepredetermined threshold SDANN or predetermined threshold SDANN slope todetect smaller changes that may be indicative of a worsening conditionof HF.

In some examples, the plurality of sensors 430 includes a heart soundsensor. The heart sound sensor produces a heart sound signalrepresentative of mechanical activity of the heart of the subject.Because ischemia is associated with a decrease in ventricular chambercontractility, ischemia is correlated to a decrease in the loudness ofthe S1 heart sound. In some examples, if the co-morbidity informationindicates a co-morbidity for the subject (e.g., MI), the signalselection circuit 415 selects the heart sound sensor signal to monitor.A description of systems and methods for monitoring heart sounds isfound in U.S. Patent Application Publication No. 20060282000, entitled“Ischemia Detection Using a Heart Sound Sensor,” filed on Jun. 8, 2005,which is incorporated herein by reference in its entirety. In someexamples, if the co-morbidity information indicates a co-morbidity forthe subject, the threshold adjustment circuit 420 may lower thenecessary threshold decrease in the sensor signal to detect theindication of a worsening condition of HF.

In certain examples, the decision circuit 425 may establish a baselinesignal for a received sensor signal (e.g., a heart sound signal). Thedecision circuit 425 may determine that the event associated withworsening HF occurred when a change in a measurement derived from asensor signal differs from an established measurement baseline value bymore than a detection threshold change value. The threshold adjustmentcircuit 420 may decrease the detection threshold change value based onan indicated co-morbidity (e.g., MI).

In some examples, the plurality of sensors 430 includes a weight sensor.The sensor signal selection circuit 415 may select a weight sensorsignal to monitor according to an indicated co-morbidity (e.g.,diabetes). The threshold adjustment circuit 420 may decrease a necessarydetection threshold change value according to the indicatedco-morbidity.

In some examples, the plurality of sensors 430 includes a blood pressuresensor. In some examples, the blood pressure sensor is included in anexternal device, such as a sphygmomanometer or a finger-cuff sensor, incommunication with the system 400. In some examples, the plurality ofsensors 430 includes an implantable cardiac pressure sensor. Animplantable cardiac pressure sensor can be used to measure chamberpressure of the left ventricle. In an example, a pressure sensor isimplanted in a coronary vessel to determine left ventricle pressure bydirect measurement of coronary vessel pressure. A description of systemsand methods that use such an implantable pressure sensor is found inSalo et al., U.S. Pat. No. 6,666,826, entitled “METHOD AND APPARATUS FORMEASURING LEFT VENTRICULAR PRESSURE,” filed Jan. 4, 2002, which isincorporated herein by reference in its entirety. Other cardiac pressuresensors examples include a right ventricle (RV) chamber pressure sensor,a pulmonary artery pressure sensor, and a left atrial chamber pressuresensor. The sensor signal selection circuit 415 may select a weightsensor signal to monitor according to an indicated co-morbidity (e.g.,hypertension, MI). The threshold adjustment circuit 420 may decrease anecessary detection threshold change value according to the indicatedco-morbidity.

In some examples, the plurality of sensors 430 includes a respirationsensor to provide a signal representative of respiration of the subject.The respiration sensor produces a respiration signal, such as anelectrical or optical respiration signal, that includes informationabout the respiration of the subject. In certain examples, therespiration sensor can include an implantable sensor including at leastone of an accelerometer, an impedance sensor, and a pressure sensor. Therespiration signal can include any signal indicative of the respirationof the subject, such as inspiration, expiration, or any combination,permutation, or component of the respiration of the subject. An exampleof an implantable respiration sensor is a transthoracic impedance sensorto measure minute respiration volume. An approach to measuringtransthoracic impedance is described in Hartley et al., U.S. Pat. No.6,076,015, “Rate Adaptive Cardiac Rhythm Management Device UsingTransthoracic Impedance,” filed Feb. 27, 1998, which is incorporatedherein by reference in its entirety. In some examples, the plurality ofsensors 430 includes an intra-thoracic impedance sensor (ITTI). Thesensor signal selection circuit 415 may select a respiration signal tomonitor according to an indicated co-morbidity (e.g., COPD, apnea). Thethreshold adjustment circuit 420 may decrease a necessary detectionthreshold change value according to the indicated co-morbidity.

In some examples, the system 400 includes a pulmonary arterial pressure(PAP) sensor. A signal produced by an implantable PAP sensor can be usedto detect a reduction in blood supply to a portion of the heart. Anapproach for detecting a reduction in blood supply to a portion of theheart using PA pressure is described in Zhang et al., commonly assigned,co-pending, U.S. patent application Ser. No. 11/624,974, entitled“Ischemia Detection Using Pressure Sensor,” filed Jan. 19, 2007, whichis incorporated herein by reference in its entirety. The sensor signalselection circuit 415 may select a respiration signal to monitoraccording to an indicated co-morbidity (e.g., hypertension, MI). Thethreshold adjustment circuit 420 may decrease a necessary detectionthreshold change value according to the indicated co-morbidity.

In some examples, the system 400 includes a cardiac stroke volume sensorto produce a signal representative of the cardiac stroke volume of thesubject. Examples of stroke volume sensing are discussed in Salo et al.,U.S. Pat. No. 4,686,987, “Biomedical Method And Apparatus ForControlling The Administration Of Therapy To A Patient In Response ToChanges In Physiologic Demand,” filed Mar. 29, 1982, which isincorporated herein by reference in its entirety. If the co-morbidityinformation indicates a co-morbidity for the subject (e.g.,hypertension, MI), the sensor signal selection circuit 415 may select arespiration signal to monitor according to an indicated co-morbidity(e.g., hypertension, MI). The threshold adjustment circuit 420 maydecrease the signal feature threshold (e.g., amplitude) necessary todetect the indication of a worsening condition of HF according to theindicated co-morbidity.

In some examples, the system 400 includes an air flow sensor. An exampleof an air flow sensor includes an air flow sensor implantable into thetrachea of the subject. Descriptions of implantable air flow sensors arefound in Piaget et al., U.S. patent application Ser. No. 11/379,396,“Implanted Air Passage Sensor,” filed on Apr. 20, 2006, which isincorporated herein by reference in its entirety. In some examples, thesensor signal selection circuit 415 selects to monitor an air flowsensor signal according to an indicated co-morbidity (e.g., COPD). Thethreshold adjustment circuit 420 may decrease the signal featurethreshold necessary to detect the indication of a worsening condition ofHF according to the indicated co-morbidity.

According to some examples, the memory 410 includes informationindicating a severity of the co-morbidity. For example, the informationmay indicate that the subject's diabetes is mild, moderate, or severe.The sensor signal selection circuit 415 may use the co-morbidity andseverity information to determine a combination of sensor signals tomonitor. For example, if the information indicates that the subject hassevere diabetes, the sensor signal selection circuit 415 may select tomonitor both a signal produced by a glucose sensor that monitors a levelof glucose in the subject's blood or interstitial fluid and an activitysensor signal. Other chemical sensors may be used to monitor otherco-morbidities of the subject. An approach to providing a chemicalsensor in a coronary sinus is found in Kane et al., U.S. patentapplication Ser. No. 11/383,933, entitled, “Implantable Medical Devicewith Chemical Sensor and Related Methods, filed May 17, 2006, which isincorporated herein by reference in its entirety.

In some examples, the threshold adjustment circuit 420 includes aweighting circuit 435 to weight physiologic information from a sensorsignal according to the co-morbidity and severity information. Theweighting may be based on the presence and/or the severity of certainco-morbidities or patient conditions and may be used to further adjust adetection threshold of the selected sensor signal. For example, theweighting circuit 435 may assign a low weight to an ITTI sensor and ahigh weight to an air flow sensor if the co-morbidity informationincludes an indication of COPD for the subject. Thus, the thresholdadjustment circuit 420 may weight information from a first sensor signaldifferently than information from a second sensor signal using theco-morbidity and severity information. If the information includes anindication of severe COPD for the subject, the threshold adjustmentcircuit 420 may adjust the detection threshold to be more sensitive(e.g., a higher threshold of air flow) than if the subject had mildCOPD. Thus, the threshold adjustment circuit 420 may use a rule basedapproach to adjust a detection threshold of the selected sensor signalaccording to the indicated co-morbidity and co-morbidity severity.

In some examples, the threshold adjustment circuit 420 includes aseverity index circuit 440 that calculates a severity index for thesubject using the co-morbidity and severity data.

TABLE 1 Co-morbidity No (0) Mild (1) Moderate (2) Severe (3) DM x HTN xApnea x MI x COPD x

Table 1 includes a list of example co-morbidities and indicates severityof the co-morbidity; from not having the co-morbidity (No) to having asevere case of the co-morbidity. The severity is provided a score. Inthe example, the score varies from 0 to 3. In some examples, the systemincludes a user interface (not shown). A table, such as Table 1, isfilled in by a physician when enabling HF detection criteria in thesystem 400. In certain examples, the severity index circuit 440calculates a total severity index I by summing all of the severityscores.

The severity index may be used by the threshold adjustment circuit 420to adjust a detection threshold of one or more sensor signals. This isshown in Table 2. The higher the severity index, the more sensitive thethreshold is adjusted by the threshold adjustment circuit 420.

TABLE 2 Total severity Adjust Sensor index I Threshold by 1 10% 2 20% 330% 4 40% 5 50%

For example, assume the co-morbidity information indicates theco-morbidities and severities of Table 1. The Total Severity Index forthe subject is 5 and the threshold adjustment circuit 420 adjusts thedetection threshold for a sensor by 50% to make the threshold moresensitive to detect events associated with HF. Further assume the system400 includes a heart sound sensor, the decision circuit 425 establishesa baseline signal for the heart sound signal, and the decision circuit425 uses a decrease in signal amplitude of the S1 heart sound to detectan event associated with HF. Because of the indicated co-morbidities andthe severity index, the threshold adjustment circuit 420 would reducethe threshold necessary to alert for an impending HF event. The decisioncircuit 425 applies the adjusted threshold to the selected sensor signalto determine whether an HF event occurred. In certain examples, thethreshold is applied to a composite signal obtained from multiplesignals from the sensors. In certain examples, the threshold adjustmentcircuit 420 implements fuzzy logic rules to blend weighted sensorsignals and severity indexes to arrive at an adjusted detectionthreshold.

In some examples, the weighting circuit 435 weights the information froma sensor signal according to the severity index. In the example of Table2, because the subject has moderate MI and no indication ofhypertension, the weighting circuit 435 may weight a sensor signalreceived from a heart sound sensor greater than a sensor signal receivedfrom a blood pressure sensor. The decision circuit 425 weights thesignal from the heart sound signal greater when determining whether anHF event occurred. Thus, in some examples the system 400 is rule based,and the threshold adjustment circuit 420, in conjunction with the sensorsignal selection circuit 415, uses a rule to adjust a detectionthreshold of the selected sensor signal according to an indicatedco-morbidity.

FIG. 5 is a flow diagram of an example of a method 500 to monitor one ormore hemodynamic parameters of a subject. At block 505, a plurality ofsensor signals is received. Each sensor signal includes physiologicinformation, and at least one of the sensor signals is provided by animplantable sensor.

At block 510, at least one sensor signal is selected to monitoraccording to a co-morbidity of a subject indicated in storedco-morbidity information. In some examples, the co-morbidity informationis included in electronic medical records (EMR), and the data isaccessed by a first device (e.g., a medical device programmer) from asecond remote device (e.g., a server) via a communication network suchas the internet or a cell phone network. In some examples, theco-morbidity information includes an indication of the severity of aco-morbidity and one or more sensor signals are selected using theco-morbidity and the severity information. In certain examples, theco-morbidity information is stored and/or changed periodically by aphysician during patient follow up visits using the programmer or theremote device.

In some examples, selecting the sensor signal includes calculating aseverity index at block 511. In certain examples, the stored informationincludes a cardiovascular condition of the subject and at least onesensor signal is selected to monitor according to the cardiovascularcondition.

At block 515, a detection threshold of the selected sensor signal isautomatically adjusted according to the indicated co-morbidity. In someexamples, the detection threshold is adjusted using the severity index.In some examples, the physiologic information from a sensor signal isweighted according to the co-morbidity and the co-morbidity severityindicated in the stored co-morbidity information at block 516.

At block 520, the detection threshold is applied to the selected sensorsignal to determine whether an event associated with worsening HFoccurred. In some examples, a plurality of sensor signals is used todetermine whether the HF event occurred. In some examples, the sensorsignals (or the physiologic information represented by the sensorsignals) are weighted, and whether the HF event occurred is determinedusing the weighted signals. At block 525, an indication is provided to auser or process of whether the event associated with worsening HFoccurred. In some examples, the indication is displayed to a user on anexternal device. In some examples, the indication is communicated to aprocess from a first device to a second device.

FIG. 6 is another block diagram of another example of a system 600 tomonitor one or more hemodynamic parameters of a subject. The system 600includes an IMD 660. The IMD 660 includes a sensor signal processor 605and is electrical communication with at least one sensor 630. The sensor630 may be integral to the IMD 660, may be coupled to the IMD 660 via alead or other wired connection, or may be a second IMD that communicateswirelessly with the first IMD 660. The sensor may provide any of thetype of sensor signals described herein to the sensor signal processor605.

The system 600 also includes an external device 655. The external device655 includes a communication circuit 650 to communicate wirelessly withthe IMD 660. In some examples, the external device 655 includes an IMDprogrammer. In some examples, the external device 655 includes a remotecomputer that is configured to communicate with the IMD over a network.In certain examples, the remote computer is a remote server thatcommunicates with the IMD 660 via a third device, such as a repeaterlocated near the subject for example.

The external device 655 also includes a memory 610 and a secondprocessor 665. The memory includes information indicating a co-morbidityof a subject. In some examples, the memory 610 includes an indication ofthe severity of the co-morbidity and/or an indication of acardiovascular condition of the subject.

The second processor 665 is in electrical communication with thecommunication circuit 650 and the memory 610, and includes a sensorsignal selection circuit 615, a threshold adjustment circuit 620, and adecision circuit 625. The sensor signal selection circuit 615 selects asensor signal to monitor from among a plurality of sensor signals,according a co-morbidity indicated in the memory 610. The thresholdadjustment circuit 620 adjusts a detection threshold of the selectedsensor signal according to the indicated co-morbidity. The decisioncircuit 625 applies the adjusted detection threshold to a selectedsensor signal to determine whether an event associated with worsening HFoccurred in the subject.

The external device 655 also includes a response circuit 645 inelectrical communication with the second processor. The response circuit645 provides a specified response when the event associated withworsening HF is declared. In some examples, the external device 655includes a display in electrical communication with the response circuit645, and the response circuit 645 displays an indication that an HFevent occurred. In some examples, the response circuit 645 communicatesthe indication to another device, such as a remote server for example.In some examples, the response circuit initiates a therapy provided tothe subject by the IMD 660 when an HF event occurs.

In some examples, the system 600 is rule based, and the thresholdadjustment circuit 620, in conjunction with the sensor signal selectioncircuit 615, uses a rule to adjust a detection threshold of a selectedsensor signal according to an indicated co-morbidity. In certainexamples, the external device 655 includes a user interface 670 inelectrical communication with the second processor 665. The userinterface 670 may include a display and a device to receive user input,such as a keyboard, keypad, touch screen, or computer mouse.

The second processor 665 may include a rule development circuit 675. Therule development circuit 675 develops the rule used by the thresholdadjustment circuit 620 to adjust the detection threshold of a sensorsignal. In certain examples, a physician enters co-morbidity datathrough the user interface 670 such as by filling out a table likeTable 1. In certain examples, the rule development circuit 675 includesa decision tree, such as a series of IF-THEN statements implemented insoftware or firmware for example. The rule development circuit 675traverses the decision tree to arrive at an adjustment of the detectionthreshold.

In certain examples, the rule development circuit 675 allows a physicianto create a customizable rule through the user interface 670. Forexample, the threshold adjustment circuit 620 may include a weightingcircuit and/or a severity index circuit as described herein. The ruledevelopment circuit 675 may allow the physician to assign weights topsensor signals based on the physician's professional experience. Incertain examples, the rule development circuit 675 allows the physicianto assign detection thresholds to sensor signals according to acalculated severity index.

The external device 655 may also receive sensor signals from otherexternal sensors. The external device may also use pre-recorded datareceived from sensors and apply an adjusted threshold to detect an HFevent from the recorded data.

The different circuits described may be allocated differently between anIMD and an external device and still perform their indicated functions.To which device they are allocated would be a design-based decisionbased on factors that include cost, size of the IMD, and battery life ofthe IMD battery.

FIG. 7 is another block diagram of yet another example of a system 700to monitor one or more hemodynamic parameters of a subject. The system700 includes an external device 755 and an IMD 760. The external device755 includes a communication circuit 750 to communicate wirelessly withthe IMD 760, a memory 710 that includes an indication of a co-morbidityof the subject, and a second processor 765. The second processor 765includes a threshold adjustment circuit 720.

The IMD 760 includes a sensor signal processor 705 in electricalcommunication with at least one sensor 730. The IMD 760 also includes athird processor 780. The third processor 780 receives a sensor detectionthreshold from the threshold adjustment circuit 720 via thecommunication circuit 750. The third processor 780 includes a sensorsignal selection circuit 715 and a decision circuit 725. The decisioncircuit 725 applies the received detection threshold to a sensor signalselected by the sensor signal selection circuit 715 to determine whetheran event associated with worsening HF occurred in the subject. The IMD760 further includes a response circuit 745 in electrical communicationwith the decision circuit 725 to provide a specified response when theevent associated with worsening HF is declared.

In some examples, the external device 755 includes a user interface 770in electrical communication with the second processor 765. The secondprocessor 765 includes a rule development circuit 775 to develop a rulevia the user interface 770. The threshold adjustment circuit 720, inconjunction with the sensor signal selection circuit 715, uses the ruleto adjust a detection threshold of a selected sensor signal according toan indicated co-morbidity and to selectively weight information from theselected sensor signal.

ADDITIONAL NOTES

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which theinvention can be practiced. These embodiments are also referred toherein as “examples.” All publications, patents, and patent documentsreferred to in this document are incorporated by reference herein intheir entirety, as though individually incorporated by reference. In theevent of inconsistent usages between this document and those documentsso incorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended, that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim are still deemed to fall within thescope of that claim. Moreover, in the following claims, the terms“first,” “second,” and “third,” etc. are used merely as labels, and arenot intended to impose numerical requirements on their objects.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods can include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code can include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, the code may be tangibly stored on one ormore volatile or non-volatile computer-readable media during executionor at other times. These computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAM's), read onlymemories (ROM's), and the like.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments can be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to complywith 37 C.F.R. §1.72(b), to allow the reader to quickly ascertain thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims. Also, in the above Detailed Description,various features may be grouped together to streamline the disclosure.This should not be interpreted as intending that an unclaimed disclosedfeature is essential to any claim. Rather, inventive subject matter maylie in less than all features of a particular disclosed embodiment.Thus, the following claims are hereby incorporated into the DetailedDescription, with each claim standing on its own as a separateembodiment. The scope of the invention should be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. An apparatus comprising: a sensor signalprocessor configured to receive a plurality of sensor signals, wherein asensor signal includes physiologic information of a subject; a memory,including information indicating a co-morbidity of the subject; a sensorsignal selection circuit, in electrical communication with the sensorsignal processor, configured to select one or more sensor signals tomonitor from among the plurality of sensor signals according to anindicated co-morbidity in the memory; and a decision circuit configuredto determine heart failure (HF) status associated with worsening heartfailure (HF) of the subject using the selected combination of sensorsignals and generate an indication of the HF status.
 2. The apparatus ofclaim 1, wherein the sensor signal selection circuit is configured to:enable a default combination of the physiological sensor signals tomonitor to detect an event associated with worsening HF when aco-morbidity is not indicated; and add or remove one or more sensorsignals to the default combination of sensor signals according to theindicated co-morbidity.
 3. The apparatus of claim 2, including athreshold adjustment circuit configured to: set one or more defaultsensor signal detection thresholds for the selected combination ofsensor signals to detect the event associated with worsening HF; andchange the one or more detection thresholds according to the indicatedco-morbidity.
 4. The apparatus of claim 1, wherein the informationindicating a co-morbidity of a subject includes co-morbidity severityinformation, and wherein the sensor signal selection circuit isconfigured to select a combination of the physiological sensor signalsto monitor when the indicated severity of the co-morbidity is a firstseverity level and select a second combination of the physiologicalsensor signals when the indicated severity of the co-morbidity is asecond severity level.
 5. The apparatus of claim 1, wherein the sensorsignal selection circuit is configured to select sensing of a heartsound signal according to the indicated co-morbidity, and detect anevent associated with worsening HF at least in part using the heartsound signal.
 6. The apparatus of claim 1, wherein the sensor signalselection circuit is configured to select a first combination of sensorsignals to monitor when diabetes of the subject is indicated and selecta second different combination of sensor signals to monitor when aco-morbidity different from diabetes is indicated.
 7. The apparatus ofclaim 1, wherein the sensor signal selection circuit is configured toselect a first combination of sensor signals to monitor whenhypertension is indicated and select a second different combination ofsensor signals to monitor when the indicated co-morbidity is differentthan hypertension.
 8. The apparatus of claim 1, wherein the sensorsignal selection circuit is configured to select a first combination ofsensor signals to monitor when myocardial infarction (MI) is indicatedand select a second different combination of sensor signals to monitorwhen the indicated co-morbidity is different than MI.
 9. The apparatusof claim 1, wherein the sensor signal selection circuit is configured toselect a first combination of sensor signals to monitor when apnea orhypopnea is indicated and select a second different combination ofsensor signals to monitor when the indicated co-morbidity is differentthan apnea or hypopnea.
 10. The apparatus of claim 1, wherein the sensorsignal selection circuit is configured to select a first combination ofsensor signals to monitor when chronic-obstructive pulmonary disorder(COPD) is indicated and select a second different combination of sensorsignals to monitor when the indicated co-morbidity is different thanCOPD.
 11. A method of controlling operation of a medical device, themethod comprising: receiving a plurality of sensor signals at themedical device, each sensor signal including physiologic information;selecting one or more sensor signals to monitor from among the pluralityof sensor signals available according to a co-morbidity of a subjectindicated in stored co-morbidity information; and determining heartfailure (HF) status associated with worsening heart failure (HF) of thesubject using the selected combination of sensor signals and generate anindication of the HF status.
 12. The method of claim 11, including:enabling a default combination of the sensor signals to monitor todetect the event associated with worsening HF when the co-morbidityinformation indicates no co-morbidity; and adding or removing one ormore sensor signals to the default combination of sensor signalsaccording to indicated co-morbidity in the co-morbidity information. 13.The method of claim 12, including: setting one or more default sensorsignal detection thresholds for the selected combination of sensorsignals to detect the event associated with worsening HF; and changingthe one or more detection thresholds according to the indicatedco-morbidity.
 14. The method of claim 11, including selecting a firstcombination of the sensor signals when the stored co-morbidityinformation indicates severity of the co-morbidity of the subject is afirst severity level, and selecting a second different combination ofthe sensor signals when the stored co-morbidity information indicatesseverity of the co-morbidity of the subject is a second severity level.15. The method of claim 11, including selecting sensing of a heart soundsignal according to the indicated co-morbidity, and detecting an eventassociated with worsening HF at least in part using the heart soundsignal.
 16. The method of claim 11, including selecting a firstcombination of sensor signals to monitor when diabetes of the subject isindicated and selecting a second different combination of sensor signalsto monitor when a co-morbidity different from diabetes is indicated. 17.The method of claim 11, including selecting a first combination ofsensor signals to monitor when hypertension is indicated and select asecond different combination of sensor signals to monitor when theindicated co-morbidity is different than hypertension.
 18. The method ofclaim 11, including selecting a first combination of sensor signals tomonitor when myocardial infarction (MI) is indicated and selecting asecond different combination of sensor signals to monitor when theindicated co-morbidity is different than MI.
 19. The method of claim 11,including selecting a first combination of sensor signals to monitorwhen apnea or hypopnea is indicated and selecting a second differentcombination of sensor signals to monitor when the indicated co-morbidityis different than apnea or hypopnea.
 20. The method of claim 11,including selecting a first combination of sensor signals to monitorwhen chronic-obstructive pulmonary disorder (COPD) is indicated andselecting a second different combination of sensor signals to monitorwhen the indicated co-morbidity is different than COPD.